{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目介绍\n",
    "\n",
    "## 项目人：潘丽璇\n",
    "\n",
    "* 项目时间：6月28日-7月3日\n",
    "\n",
    "\n",
    "## 数据来源\n",
    "\n",
    "* 和鲸社区：选用boss直聘网站的数据分析职位的招聘数据  \n",
    "\n",
    "-----\n",
    "\n",
    "## 数据源介绍\n",
    "\n",
    "* 项目主要使用“job.csv”文件作为数据源，其中数据文件的主要栏位有职位、城市、公司、薪资、学历、工作经验、行业标签。  \n",
    "\n",
    "\n",
    "> 其中本项目所使用的可计算的栏位为最低薪资、最高薪资、平均薪资、奖金率。\n",
    "\n",
    ">本项目所使用的可分类的栏位为职位、城市、学历、工作经验、行业标签。  \n",
    "\n",
    "## 数据分析目标\n",
    "\n",
    "![目标](img/数据分析目标.png)\n",
    "\n",
    "[数据分析目标](http://naotu.baidu.com/file/879df08193b02242e219c71829a020ed?token=acf51578fe1a1994)\n",
    "\n",
    "\n",
    "## 数据分析结果价值宣言\n",
    "\n",
    "### 数据分析的优势\n",
    "* 及时性高、可信度高、相关性强、安全性高\n",
    "\n",
    "### 数据分析行业的就业前景\n",
    "* 随着互联网和大数据的发展，数据分析市场需求大。\n",
    "* 高于行业平均水平的薪资。\n",
    "* 发展前景和选择性非常广。\n",
    "\n",
    "\n",
    "### 本人研究数据分析行业的就业前景的原因\n",
    "* 这学期开始接触数据分析，我对这个行业也慢慢从一开始“抵触复杂的代码”产生的害怕抗拒心理，转变成如今对数据分析有了兴趣，并且有以后从事这方面的兴趣和想法。看到一个又一个bug被自己解决的成就感，以及看到自己做出来的数据分析成果的满足感，还有通过数据分析，我对数据有了更深的理解。\n",
    "* 我想了解数据分析行业的职位、薪资以及在各大城市的招聘情况是如何的，为以后作准备。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模块准备\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings \n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import cufflinks as cf\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from scipy.stats import norm,mode\n",
    "warnings.filterwarnings('ignore')\n",
    "cf.set_config_file(offline=True)\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # 读取数据\n",
    "# df = pd.read_csv (\"job.csv\", encoding = \"utf8\", sep=\"\\t\")\n",
    "# df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·亚运村</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·太阳宫</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>人力资源服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·鸟巢</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>50-80K·14薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京·海淀区·西北旺</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3045 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位           城市      公司          薪资  学历   工作经验    行业标签\n",
       "0          数据分析   北京·朝阳区·亚运村  中信百信银行  25-40K·15薪  本科  5-10年      银行\n",
       "1          数据分析   北京·朝阳区·太阳宫  BOSS直聘  25-40K·16薪  博士   1-3年  人力资源服务\n",
       "2          数据分析    北京·朝阳区·鸟巢    京东集团  50-80K·14薪  本科   3-5年    电子商务\n",
       "3          数据分析    北京·海淀区·清河     一亩田      15-25K  本科   3-5年     O2O\n",
       "4         数据分析岗   北京·海淀区·西北旺    建信金科  20-40K·14薪  硕士  5-10年      银行\n",
       "...         ...          ...     ...         ...  ..    ...     ...\n",
       "3040    助理数据分析员  南京·建邺区·雨润大街      万得        4-6K  本科   经验不限    数据服务\n",
       "3041  数据分析师（经济）    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "3042  （金融）数据分析员    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "3043      数据分析员    南京·建邺区·奥体      万得        4-6K  本科   1年以内    数据服务\n",
       "3044    助理数据分析员  南京·建邺区·雨润大街      万得        4-8K  本科   经验不限    数据服务\n",
       "\n",
       "[3045 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#由于原数据没有列名，所以设置列名\n",
    "df = pd.read_csv('job.csv',header=None,names=['职位','城市','公司','薪资','学历','工作经验','行业标签'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据整体情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·亚运村</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·太阳宫</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>人力资源服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·鸟巢</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>50-80K·14薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京·海淀区·西北旺</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      职位          城市      公司          薪资  学历   工作经验    行业标签\n",
       "0   数据分析  北京·朝阳区·亚运村  中信百信银行  25-40K·15薪  本科  5-10年      银行\n",
       "1   数据分析  北京·朝阳区·太阳宫  BOSS直聘  25-40K·16薪  博士   1-3年  人力资源服务\n",
       "2   数据分析   北京·朝阳区·鸟巢    京东集团  50-80K·14薪  本科   3-5年    电子商务\n",
       "3   数据分析   北京·海淀区·清河     一亩田      15-25K  本科   3-5年     O2O\n",
       "4  数据分析岗  北京·海淀区·西北旺    建信金科  20-40K·14薪  硕士  5-10年      银行"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3045, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3045 entries, 0 to 3044\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   职位      3045 non-null   object\n",
      " 1   城市      3045 non-null   object\n",
      " 2   公司      3045 non-null   object\n",
      " 3   薪资      3045 non-null   object\n",
      " 4   学历      3045 non-null   object\n",
      " 5   工作经验    3045 non-null   object\n",
      " 6   行业标签    3045 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 166.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查找有无重复数据\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 当keep=False时，就是去掉所有的重复行 \n",
    "* 当keep=‘first'时，就是保留第一次出现的重复行 \n",
    "* 当keep='last'时就是保留最后一次出现的重复行\n",
    ">参考数据：[pandas DataFrame 删除重复的行的实现方法](https://www.jb51.net/article/155577.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查还有没有重复的数据\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 7)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "职位      0\n",
       "城市      0\n",
       "公司      0\n",
       "薪资      0\n",
       "学历      0\n",
       "工作经验    0\n",
       "行业标签    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查每列是否有缺失值以及缺失值的数量\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·亚运村</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·太阳宫</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>人力资源服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·鸟巢</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>50-80K·14薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京·海淀区·西北旺</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3043</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位           城市      公司          薪资  学历   工作经验    行业标签\n",
       "0          数据分析   北京·朝阳区·亚运村  中信百信银行  25-40K·15薪  本科  5-10年      银行\n",
       "1          数据分析   北京·朝阳区·太阳宫  BOSS直聘  25-40K·16薪  博士   1-3年  人力资源服务\n",
       "2          数据分析    北京·朝阳区·鸟巢    京东集团  50-80K·14薪  本科   3-5年    电子商务\n",
       "3          数据分析    北京·海淀区·清河     一亩田      15-25K  本科   3-5年     O2O\n",
       "4         数据分析岗   北京·海淀区·西北旺    建信金科  20-40K·14薪  硕士  5-10年      银行\n",
       "...         ...          ...     ...         ...  ..    ...     ...\n",
       "3040    助理数据分析员  南京·建邺区·雨润大街      万得        4-6K  本科   经验不限    数据服务\n",
       "3041  数据分析师（经济）    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "3042  （金融）数据分析员    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "3043      数据分析员    南京·建邺区·奥体      万得        4-6K  本科   1年以内    数据服务\n",
       "3044    助理数据分析员  南京·建邺区·雨润大街      万得        4-8K  本科   经验不限    数据服务\n",
       "\n",
       "[3000 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗职位数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**考虑到数据中有实习岗位，实习岗薪资按天算，不具有太大的参考价值，故删除包含实习的数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·亚运村</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·太阳宫</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>人力资源服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·朝阳区·鸟巢</td>\n",
       "      <td>京东集团</td>\n",
       "      <td>50-80K·14薪</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京·海淀区·清河</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京·海淀区·西北旺</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京·建邺区·奥体</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京·建邺区·雨润大街</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2926 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位           城市      公司          薪资  学历   工作经验    行业标签\n",
       "0          数据分析   北京·朝阳区·亚运村  中信百信银行  25-40K·15薪  本科  5-10年      银行\n",
       "1          数据分析   北京·朝阳区·太阳宫  BOSS直聘  25-40K·16薪  博士   1-3年  人力资源服务\n",
       "2          数据分析    北京·朝阳区·鸟巢    京东集团  50-80K·14薪  本科   3-5年    电子商务\n",
       "3          数据分析    北京·海淀区·清河     一亩田      15-25K  本科   3-5年     O2O\n",
       "4         数据分析岗   北京·海淀区·西北旺    建信金科  20-40K·14薪  硕士  5-10年      银行\n",
       "...         ...          ...     ...         ...  ..    ...     ...\n",
       "2921    助理数据分析员  南京·建邺区·雨润大街      万得        4-6K  本科   经验不限    数据服务\n",
       "2922  数据分析师（经济）    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "2923  （金融）数据分析员    南京·建邺区·奥体      万得        4-6K  本科   经验不限    数据服务\n",
       "2924      数据分析员    南京·建邺区·奥体      万得        4-6K  本科   1年以内    数据服务\n",
       "2925    助理数据分析员  南京·建邺区·雨润大街      万得        4-8K  本科   经验不限    数据服务\n",
       "\n",
       "[2926 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shixi=df['职位'].str.contains('实习')\n",
    "df=df[~shixi]\n",
    "df.reset_index(drop=True,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗城市数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**城市列包含的值太杂，且本项目分析不需要用到详细地址。鉴于城市数据不规范，所以进行处理，全部转换为城市名。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['城市']=df['城市'].str[:2]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['北京', '上海', '广州', '深圳', '杭州', '天津', '苏州', '武汉', '厦门', '长沙', '成都',\n",
       "       '郑州', '重庆', '青岛', '南京'], dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查城市有无错误和不规范\n",
    "df['城市'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗薪资数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['25-40K·15薪', '25-40K·16薪', '15-25K', '20-40K·14薪', '15-30K·14薪',\n",
       "       '20-30K', '15-25K·14薪', '40-55K·13薪', '20-35K', '30-55K·13薪',\n",
       "       '20-40K·16薪', '35-40K·15薪', '15-30K', '25-50K·14薪', '25-35K·14薪',\n",
       "       '15-25K·16薪', '15-28K·14薪', '18-28K', '30-50K·13薪', '20-35K·14薪',\n",
       "       '15-28K', '20-30K·13薪', '30-50K·16薪', '18-30K·14薪', '18-22K·15薪',\n",
       "       '25-45K·16薪', '13-25K', '14-25K·14薪', '18-35K·14薪', '25-45K·14薪',\n",
       "       '25-40K', '15-26K·13薪', '12-24K', '25-45K', '20-40K', '20-30K·15薪',\n",
       "       '15-25K·15薪', '25-40K·17薪', '20-30K·14薪', '18-35K', '18-27K',\n",
       "       '30-45K', '20-40K·15薪', '20-30K·16薪', '25-30K·15薪', '17-27K',\n",
       "       '28-50K·14薪', '25-35K', '30-60K·14薪', '30-55K', '15-22K', '30-50K',\n",
       "       '30-50K·14薪', '30-60K·13薪', '25-50K·15薪', '13-26K·16薪', '25-50K',\n",
       "       '12-24K·14薪', '17-25K·15薪', '18-25K·15薪', '28-40K·16薪', '30-40K',\n",
       "       '28-40K·13薪', '20-25K·16薪', '25-30K·14薪', '15-30K·15薪',\n",
       "       '25-40K·14薪', '30-45K·14薪', '20-35K·16薪', '15-30K·16薪', '25-26K',\n",
       "       '20-25K', '25-50K·16薪', '18-35K·16薪', '18-25K·14薪', '25-30K',\n",
       "       '19-35K', '12-22K·14薪', '28-45K·14薪', '18-30K', '18-25K',\n",
       "       '15-25K·13薪', '15-25K·17薪', '15-30K·13薪', '18-30K·15薪',\n",
       "       '25-40K·13薪', '25-30K·13薪', '20-35K·15薪', '18-24K', '30-60K',\n",
       "       '18-30K·13薪', '16-25K·13薪', '20-28K·15薪', '15-20K·13薪',\n",
       "       '15-20K·14薪', '12-18K', '11-20K', '20-40K·13薪', '14-28K',\n",
       "       '11-17K·13薪', '15-20K', '9-14K', '12-15K', '11-22K', '10-15K',\n",
       "       '12-20K', '12-17K', '9-13K·13薪', '10-15K·14薪', '10-15K·13薪',\n",
       "       '7-12K·14薪', '10-11K', '6-9K', '10-12K', '20-25K·14薪', '8-10K·13薪',\n",
       "       '9-13K·14薪', '7-10K', '7-10K·13薪', '20-35K·13薪', '25-35K·16薪',\n",
       "       '30-40K·13薪', '30-50K·15薪', '12-20K·14薪', '28-55K', '23-45K',\n",
       "       '8-13K', '30-35K·15薪', '30-45K·16薪', '15-28K·15薪', '40-60K',\n",
       "       '30-35K', '12-24K·16薪', '16-30K·15薪', '11-15K·15薪', '15-16K',\n",
       "       '6-10K·13薪', '4-8K', '5-7K', '4-6K', '4-7K', '8-13K·13薪',\n",
       "       '14-20K·13薪', '18-28K·16薪', '6-8K', '35-50K', '11-18K', '6-10K',\n",
       "       '25-35K·15薪', '5-10K·13薪', '8-10K', '5-10K', '12-17K·14薪',\n",
       "       '11-20K·13薪', '10-13K·14薪', '8-12K', '13-25K·14薪', '11-22K·18薪',\n",
       "       '28-40K·14薪', '3-6K', '12-22K', '5-8K', '9-14K·16薪', '13-20K',\n",
       "       '14-20K·14薪', '15-17K·13薪', '5-6K', '6-8K·13薪', '15-17K', '3-5K',\n",
       "       '6-7K·13薪', '18-35K·15薪', '3-4K', '8-13K·14薪', '8-12K·13薪',\n",
       "       '7-12K·13薪', '4-5K', '9-14K·13薪', '5-9K', '12-18K·13薪',\n",
       "       '20-25K·15薪', '9-11K', '8-16K', '13-23K', '14-25K', '7-12K',\n",
       "       '12-15K·13薪', '3-5K·13薪', '12-24K·13薪', '16-23K', '6-10K·15薪',\n",
       "       '11-16K', '7-11K', '16-22K·13薪', '10-20K', '14-22K', '30-35K·14薪',\n",
       "       '13-22K·14薪', '5-8K·13薪', '10-15K·16薪', '5-6K·13薪', '13-25K·13薪',\n",
       "       '8-11K', '13-26K', '16-32K', '16-28K', '9-13K', '12-16K', '21-22K',\n",
       "       '20-40K·18薪', '16-30K', '11-16K·13薪', '15-22K·13薪', '18-25K·13薪',\n",
       "       '20-21K', '10-15K·15薪', '9-12K', '23-45K·16薪', '25-50K·13薪',\n",
       "       '25-30K·20薪', '35-50K·15薪', '30-40K·18薪', '15-26K', '14-28K·14薪',\n",
       "       '18-22K', '35-65K', '15-21K', '12-20K·13薪', '21-35K·16薪',\n",
       "       '15-30K·17薪', '4-9K', '9-14K·15薪', '20-40K·17薪', '18-36K',\n",
       "       '6-8K·15薪', '4-6K·13薪', '25-35K·13薪', '16-30K·14薪', '22-27K',\n",
       "       '11-18K·13薪', '18-26K', '28-50K·13薪', '35-40K', '20-24K', '17-25K',\n",
       "       '13-21K·13薪', '12-20K·17薪', '12-24K·15薪', '15-22K·14薪',\n",
       "       '12-18K·15薪', '8-13K·15薪', '24-38K', '6-11K·13薪', '6-11K', '9-15K',\n",
       "       '11-15K', '7-8K', '8-9K', '2-5K', '7-11K·13薪', '6-7K', '4-8K·13薪',\n",
       "       '3-4K·13薪', '3-7K', '12-13K·13薪', '12-17K·15薪', '7-9K',\n",
       "       '14-28K·13薪', '8-15K', '9-11K·13薪', '10-12K·13薪', '8-14K',\n",
       "       '12-18K·14薪', '4-5K·13薪', '9-14K·14薪', '12-16K·13薪', '5-8K·15薪',\n",
       "       '5-10K·14薪', '11-20K·14薪', '12-20K·15薪', '17-30K·15薪', '6-9K·14薪',\n",
       "       '15-18K·13薪', '11-22K·14薪', '12-22K·15薪', '15-23K', '18-23K',\n",
       "       '14-28K·15薪', '35-50K·14薪', '13-20K·15薪', '15-20K·15薪', '6-8K·14薪',\n",
       "       '17-30K', '7-8K·13薪', '10-13K', '4-6K·14薪', '2-4K', '6-12K',\n",
       "       '6-11K·14薪', '10-13K·13薪', '8-12K·14薪', '5-7K·13薪', '35-50K·13薪',\n",
       "       '11-12K', '4-5K·14薪', '10-13K·15薪', '27-40K', '16-25K·14薪',\n",
       "       '12-22K·13薪', '11-22K·13薪', '5-9K·13薪', '13-21K', '13-17K',\n",
       "       '11-20K·15薪', '11-19K', '14-18K', '11-20K·17薪', '3-8K', '13-18K',\n",
       "       '10-20K·18薪', '8-11K·13薪', '13-26K·14薪', '13-20K·14薪',\n",
       "       '15-16K·13薪', '11-18K·14薪', '2-6K', '8-10K·14薪', '3-5K·14薪',\n",
       "       '10-11K·16薪', '18-20K', '12-13K', '12-13K·15薪', '2-7K',\n",
       "       '8-12K·15薪', '15-30K·18薪', '6-7K·14薪', '5-8K·16薪', '18-22K·18薪',\n",
       "       '11-16K·15薪', '15-25K·20薪', '18-35K·13薪', '14-20K', '13-16K',\n",
       "       '4-7K·13薪', '10-12K·15薪', '7-14K', '12-14K', '3-7K·13薪',\n",
       "       '7-10K·14薪', '22-40K', '4-6K·15薪', '15-24K', '13-22K·16薪',\n",
       "       '26-50K', '10-18K', '6-9K·13薪', '14-15K·14薪', '9-10K', '3-6K·13薪',\n",
       "       '4-9K·13薪', '16-20K·13薪', '12-23K', '11-16K·14薪', '13-18K·13薪',\n",
       "       '12-15K·15薪', '20-28K·13薪', '6-10K·14薪', '12-17K·13薪', '13-15K',\n",
       "       '13-14K', '11-20K·16薪', '5-7K·14薪', '10-15K·17薪', '13-20K·13薪',\n",
       "       '4-9K·14薪', '17-34K', '20-25K·19薪'], dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['薪资'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. 将薪资列的值进行拆分，新增最低薪资，最高薪资两列，作为一个岗位薪资的最低值和最高值。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['最低薪资']=df['薪资'].str.extract('^(\\d+).*')+'k'\n",
    "df['最低薪资']=df['薪资'].str.extract('^(\\d+).*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['最高薪资']=df['薪资'].str.extract('^.*?-(\\d+).*')+'k'\n",
    "df['最高薪资']=df['薪资'].str.extract('^.*?-(\\d+).*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资 最低薪资 最高薪资       奖金率  平均薪资  学历   工作经验  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪   25   40  1.250000    40  本科  5-10年   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪   25   40  1.333333    43  博士   1-3年   \n",
       "3          数据分析  北京     一亩田      15-25K   15   25  1.000000    20  本科   3-5年   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪   20   40  1.166667    35  硕士  5-10年   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪   15   30  1.166667    26  本科   1-3年   \n",
       "...         ...  ..     ...         ...  ...  ...       ...   ...  ..    ...   \n",
       "2921    助理数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2924      数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   1年以内   \n",
       "2925    助理数据分析员  南京      万得        4-8K    4    8  1.000000     6  本科   经验不限   \n",
       "\n",
       "      行业标签  \n",
       "0       银行  \n",
       "1     其他行业  \n",
       "3      O2O  \n",
       "4       银行  \n",
       "5      互联网  \n",
       "...    ...  \n",
       "2921  数据服务  \n",
       "2922  数据服务  \n",
       "2923  数据服务  \n",
       "2924  数据服务  \n",
       "2925  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. 有些公司的薪资是单个值，则用最低薪资列的值填充最高薪资列。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['最高薪资'].fillna(df['最低薪资'],inplace=True)\n",
    "#传入inplace=True直接修改原对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资 最低薪资 最高薪资       奖金率  平均薪资  学历   工作经验  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪   25   40  1.250000    40  本科  5-10年   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪   25   40  1.333333    43  博士   1-3年   \n",
       "3          数据分析  北京     一亩田      15-25K   15   25  1.000000    20  本科   3-5年   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪   20   40  1.166667    35  硕士  5-10年   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪   15   30  1.166667    26  本科   1-3年   \n",
       "...         ...  ..     ...         ...  ...  ...       ...   ...  ..    ...   \n",
       "2921    助理数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2924      数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   1年以内   \n",
       "2925    助理数据分析员  南京      万得        4-8K    4    8  1.000000     6  本科   经验不限   \n",
       "\n",
       "      行业标签  \n",
       "0       银行  \n",
       "1     其他行业  \n",
       "3      O2O  \n",
       "4       银行  \n",
       "5      互联网  \n",
       "...    ...  \n",
       "2921  数据服务  \n",
       "2922  数据服务  \n",
       "2923  数据服务  \n",
       "2924  数据服务  \n",
       "2925  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. 有些公司有标明年终奖，如14薪等，故新增一列奖金率，并计算每个岗位的奖金率**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
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       "      <th>平均薪资</th>\n",
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       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
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       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资 最低薪资 最高薪资       奖金率  平均薪资  学历   工作经验  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪   25   40  1.250000    40  本科  5-10年   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪   25   40  1.333333    43  博士   1-3年   \n",
       "3          数据分析  北京     一亩田      15-25K   15   25  1.000000    20  本科   3-5年   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪   20   40  1.166667    35  硕士  5-10年   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪   15   30  1.166667    26  本科   1-3年   \n",
       "...         ...  ..     ...         ...  ...  ...       ...   ...  ..    ...   \n",
       "2921    助理数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   经验不限   \n",
       "2924      数据分析员  南京      万得        4-6K    4    6  1.000000     5  本科   1年以内   \n",
       "2925    助理数据分析员  南京      万得        4-8K    4    8  1.000000     6  本科   经验不限   \n",
       "\n",
       "      行业标签  \n",
       "0       银行  \n",
       "1     其他行业  \n",
       "3      O2O  \n",
       "4       银行  \n",
       "5      互联网  \n",
       "...    ...  \n",
       "2921  数据服务  \n",
       "2922  数据服务  \n",
       "2923  数据服务  \n",
       "2924  数据服务  \n",
       "2925  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['奖金率']=df['薪资'].str.extract('^.*?·(\\d{2})薪')\n",
    "df['奖金率'].fillna(12,inplace=True)\n",
    "# 没有年终奖的要给他们填入空缺值！！！千万严谨呜呜呜呜哭了\n",
    "df['奖金率']=df['奖金率'].astype('float64')\n",
    "df['奖金率']=df['奖金率']/12\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. 将最低薪资，最高薪资，奖金率列转换为数值形式，并以此计算出每个岗位的平均薪资作为新增列**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
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       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
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       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['最低薪资'] = df['最低薪资'].astype('int64')\n",
    "df['最高薪资'] = df['最高薪资'].astype('int64')\n",
    "df['平均薪资'] = (df['最低薪资']+df['最高薪资'])/2*df['奖金率']\n",
    "df['平均薪资'] = df['平均薪资'].astype('int64')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历   工作经验  行业标签\n",
       "0   数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科  5-10年    银行\n",
       "1   数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   1-3年  其他行业\n",
       "3   数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   3-5年   O2O\n",
       "4  数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士  5-10年    银行\n",
       "5   数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   1-3年   互联网"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
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       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#调整顺序\n",
    "cols=list(df)\n",
    "cols.insert(4,cols.pop(cols.index('最低薪资')))\n",
    "cols.insert(5,cols.pop(cols.index('最高薪资')))\n",
    "cols.insert(6,cols.pop(cols.index('奖金率')))\n",
    "cols.insert(7,cols.pop(cols.index('平均薪资')))\n",
    "df=df.loc[:,cols]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. 再次查看数据，发现极端异常值，月薪1000和月薪10万**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2880.000000</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>2880.000000</td>\n",
       "      <td>2880.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.563889</td>\n",
       "      <td>19.363889</td>\n",
       "      <td>1.055816</td>\n",
       "      <td>16.367014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.853355</td>\n",
       "      <td>12.453186</td>\n",
       "      <td>0.097943</td>\n",
       "      <td>11.077407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>22.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              最低薪资         最高薪资          奖金率         平均薪资\n",
       "count  2880.000000  2880.000000  2880.000000  2880.000000\n",
       "mean     11.563889    19.363889     1.055816    16.367014\n",
       "std       6.853355    12.453186     0.097943    11.077407\n",
       "min       2.000000     4.000000     1.000000     3.000000\n",
       "25%       6.000000     9.000000     1.000000     7.000000\n",
       "50%      10.000000    15.000000     1.000000    13.000000\n",
       "75%      15.000000    28.000000     1.083333    22.250000\n",
       "max      40.000000    65.000000     1.666667    53.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. 这些极端值数量都很少，剔除月薪小于2000大于55000的数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
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       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[(df.平均薪资>2)&(df.平均薪资<55)]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看学历是否需要清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['本科', '博士', '硕士', '不限', '大专', '高中', '中技'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['学历'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    2153\n",
       "大专     487\n",
       "硕士     142\n",
       "不限      74\n",
       "中技      10\n",
       "高中       8\n",
       "博士       6\n",
       "Name: 学历, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['学历'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_row',100) \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗工作经验数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5-10年', '1-3年', '3-5年', '经验不限', '在校/应届', '1年以内', '10年以上'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['工作经验'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5-10年', '1-3年', '3-5年', '经验不限', '在校/应届', '1年以内', '10年以上'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['工作经验'].replace('5-10年学历','5-10年',inplace=True)\n",
    "df['工作经验'].replace('3-5年学历','3-5年',inplace=True)\n",
    "df['工作经验'].replace('经验不限学历','经验不限',inplace=True)\n",
    "df['工作经验'].replace('1-3年学历','1-3年',inplace=True)\n",
    "df['工作经验'].replace('1年以内学历','1年以内',inplace=True)\n",
    "df['工作经验'].replace('经验不限中专/','经验不限',inplace=True)\n",
    "df['工作经验'].replace('1年以内中专/','1年以内',inplace=True)\n",
    "df['工作经验'].replace('1-3年中专/','1-3年',inplace=True)\n",
    "df['工作经验'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗行业标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. 观察行业标签列**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "互联网       729\n",
       "其他行业      647\n",
       "计算机软件     285\n",
       "移动互联网     269\n",
       "电子商务      247\n",
       "数据服务      111\n",
       "互联网金融      87\n",
       "游戏         80\n",
       "在线教育       75\n",
       "生活服务       72\n",
       "O2O        69\n",
       "医疗健康       68\n",
       "贸易/进出口     52\n",
       "企业服务       46\n",
       "银行         43\n",
       "Name: 行业标签, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['行业标签'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. 对数量较少且为传统行业的数据归入其他行业**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[~df['行业标签'].isin(['互联网','计算机软件','移动互联网','电子商务','数据服务',\n",
    "                           '互联网金融','游戏','在线教育','生活服务','O2O','医疗健康','贸易/进出口','企业服务','银行']),'行业标签']='其他行业'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "互联网       729\n",
       "其他行业      647\n",
       "计算机软件     285\n",
       "移动互联网     269\n",
       "电子商务      247\n",
       "数据服务      111\n",
       "互联网金融      87\n",
       "游戏         80\n",
       "在线教育       75\n",
       "生活服务       72\n",
       "O2O        69\n",
       "医疗健康       68\n",
       "贸易/进出口     52\n",
       "企业服务       46\n",
       "银行         43\n",
       "Name: 行业标签, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['行业标签'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "new_df=df\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看不同城市的平均薪资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "city=list(new_df.平均薪资.groupby(new_df['城市']).agg(['mean','median']).reset_index().iloc[:,0])\n",
    "mean=list(new_df.平均薪资.groupby(new_df['城市']).agg(['mean','median']).reset_index().iloc[:,1])\n",
    "median=list(new_df.平均薪资.groupby(new_df['城市']).agg(['mean','median']).reset_index().iloc[:,2])"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=city, \n",
    "    y=mean,             \n",
    "    name='平均值',\n",
    "    marker_color='rgb(204,102,153)'\n",
    "                \n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=city, \n",
    "    y=median,             \n",
    "    name='中位数',\n",
    "    marker_color='rgb(153,204,204)',\n",
    "   \n",
    "                \n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='不同城市的薪资对比',  # 整个图的标题\n",
    "    xaxis_tickfont_size=12,   # x轴字体大小\n",
    "    legend=dict(\n",
    "#         x=0,  # 图例的位置\n",
    "#         y=1,\n",
    "#         bgcolor='rgba(255, 255, 255, 0)',  # 图例的背景色\n",
    "        bordercolor='rgba(2, 255, 255, 0)'   # 边缘颜色\n",
    "    ),\n",
    "    barmode='group',  # 柱状图模式\n",
    "    bargap=0.4,  # 组间距离\n",
    "    bargroupgap=0  # 组内距离\n",
    "    )\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存\n",
    "py.offline.plot(fig,filename=\"各个城市的薪资状况.html\",auto_open=False)\n",
    "with open(\"各个城市的薪资状况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通过上图，可以发现各个城市的均值与中位数差距不大，但是城市之间区别非常大\n",
    "* 北上深杭这四个城市的工资水平远远高于其他城市，广州苏州稍为靠后\n",
    "* 靠前的这几个城市也是全国经济最为发达的几个城市，而且都发布在东部\n",
    "* 中西部城市薪资水平普遍低一些"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看各个薪资阶段的岗位数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2000x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(25,8),dpi=80)\n",
    "plt.hist(new_df['平均薪资'],bins=30,color='#f59311',alpha=0.3,edgecolor='k')\n",
    "plt.ylabel('岗位数',fontsize=15)\n",
    "plt.xlabel('薪资',fontsize=15)\n",
    "plt.xticks(list(range(0,60,2)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通过上图可以观察到，从总体来看，这个岗位少部分人拿着极高的工资\n",
    "* 他们拉高了整个岗位的平均水平\n",
    "* 月薪八千至两万五的岗位不在少数\n",
    "* 然而四千至六千的岗位居然是最多的，那么这些岗位主要是分布在哪些地区或行业呢，下面尝试进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 薪资在四千至六千"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>广州</td>\n",
       "      <td>健丽美诗沁</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>医疗健康</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>广州</td>\n",
       "      <td>晟田集团</td>\n",
       "      <td>4-7K</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>大专</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>广州</td>\n",
       "      <td>昭龙服饰</td>\n",
       "      <td>5-6K</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>广州</td>\n",
       "      <td>袋鼠妈妈</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>大专</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>电子商务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>数据分析专员（零售行业）</td>\n",
       "      <td>深圳</td>\n",
       "      <td>百丽</td>\n",
       "      <td>5-6K·13薪</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>5</td>\n",
       "      <td>大专</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>不限</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                职位  城市     公司        薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  工作经验  \\\n",
       "424         数据分析专员  广州  健丽美诗沁      4-6K     4     6  1.000000     5  本科  1-3年   \n",
       "426          数据分析师  广州   晟田集团      4-7K     4     7  1.000000     5  大专  1-3年   \n",
       "487         数据分析专员  广州   昭龙服饰      5-6K     5     6  1.000000     5  本科  1-3年   \n",
       "561         数据分析专员  广州   袋鼠妈妈      4-6K     4     6  1.000000     5  大专  1年以内   \n",
       "644   数据分析专员（零售行业）  深圳     百丽  5-6K·13薪     5     6  1.083333     5  大专  1年以内   \n",
       "...            ...  ..    ...       ...   ...   ...       ...   ...  ..   ...   \n",
       "2920         数据分析师  南京     万得      4-6K     4     6  1.000000     5  不限  经验不限   \n",
       "2921       助理数据分析员  南京     万得      4-6K     4     6  1.000000     5  本科  经验不限   \n",
       "2922     数据分析师（经济）  南京     万得      4-6K     4     6  1.000000     5  本科  经验不限   \n",
       "2923     （金融）数据分析员  南京     万得      4-6K     4     6  1.000000     5  本科  经验不限   \n",
       "2924         数据分析员  南京     万得      4-6K     4     6  1.000000     5  本科  1年以内   \n",
       "\n",
       "      行业标签  \n",
       "424   医疗健康  \n",
       "426   其他行业  \n",
       "487   其他行业  \n",
       "561   电子商务  \n",
       "644   其他行业  \n",
       "...    ...  \n",
       "2920  数据服务  \n",
       "2921  数据服务  \n",
       "2922  数据服务  \n",
       "2923  数据服务  \n",
       "2924  数据服务  \n",
       "\n",
       "[197 rows x 11 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "薪资_四千至六千= new_df[(new_df['平均薪资']>4) & (new_df['平均薪资']<6)]\n",
    "薪资_四千至六千"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
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       "      <th>城市</th>\n",
       "      <th>岗位数</th>\n",
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       "    <tr>\n",
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       "      <td>重庆</td>\n",
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       "      <th>11</th>\n",
       "      <td>长沙</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>青岛</td>\n",
       "      <td>25</td>\n",
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       "    城市  岗位数\n",
       "0   南京   21\n",
       "1   厦门    8\n",
       "2   天津   18\n",
       "3   广州    4\n",
       "4   成都   17\n",
       "5   杭州    1\n",
       "6   武汉   15\n",
       "7   深圳    1\n",
       "8   苏州   19\n",
       "9   郑州   34\n",
       "10  重庆   20\n",
       "11  长沙   14\n",
       "12  青岛   25"
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     },
     "execution_count": 80,
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   "source": [
    "薪资_四千至六千岗位数=pd.DataFrame(薪资_四千至六千.groupby('城市').平均薪资.count())\n",
    "new_df岗位数=薪资_四千至六千岗位数.reset_index().rename(columns={\"平均薪资\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "new_df岗位数"
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  },
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   "cell_type": "code",
   "execution_count": 81,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "datas=pd.DataFrame({\"岗位数量\":new_df岗位数[\"岗位数\"]\n",
    "                              ,\"城市\":new_df岗位数[\"城市\"]})\n",
    "fig=px.pie(datas,values=\"岗位数量\",names=\"城市\",title=\"薪资_四千至六千岗位数量的城市分布情况\",template=\"seaborn\")\n",
    "fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"薪资_四千至六千岗位数量的城市分布情况.html\",auto_open=False)\n",
    "with open(\"薪资_四千至六千岗位数量的城市分布情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 由上可知，岗位人数最多的薪资水平较低，为4-6k。\n",
    "* 同时薪资水平在4-6k的岗位主要集中分布于郑州、青岛、南京、重庆、苏州、天津、成都、武汉、长沙等中西部城市。\n",
    "* 东部地区的“数据分析”岗位薪资基本不在4-6k范围。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 薪资小于八千"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 根据调研得知大多数人找工作会把8k当作薪资的分水岭，以下分别分析薪资小于和大于8k的情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>上海</td>\n",
       "      <td>平安好学</td>\n",
       "      <td>6-9K</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>在线教育</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>广州</td>\n",
       "      <td>美丽联盟</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>广州</td>\n",
       "      <td>师大教育</td>\n",
       "      <td>5-7K</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>在线教育</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>广州</td>\n",
       "      <td>健丽美诗沁</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>医疗健康</td>\n",
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       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>广州</td>\n",
       "      <td>晟田集团</td>\n",
       "      <td>4-7K</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>大专</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>796 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市     公司    薪资  最低薪资  最高薪资  奖金率  平均薪资  学历  工作经验  行业标签\n",
       "289       数据分析师  上海   平安好学  6-9K     6     9  1.0     7  本科  1-3年  在线教育\n",
       "420      数据分析专员  广州   美丽联盟  4-8K     4     8  1.0     6  本科  1-3年  其他行业\n",
       "422       数据分析师  广州   师大教育  5-7K     5     7  1.0     6  本科  1-3年  在线教育\n",
       "424      数据分析专员  广州  健丽美诗沁  4-6K     4     6  1.0     5  本科  1-3年  医疗健康\n",
       "426       数据分析师  广州   晟田集团  4-7K     4     7  1.0     5  大专  1-3年  其他行业\n",
       "...         ...  ..    ...   ...   ...   ...  ...   ...  ..   ...   ...\n",
       "2921    助理数据分析员  南京     万得  4-6K     4     6  1.0     5  本科  经验不限  数据服务\n",
       "2922  数据分析师（经济）  南京     万得  4-6K     4     6  1.0     5  本科  经验不限  数据服务\n",
       "2923  （金融）数据分析员  南京     万得  4-6K     4     6  1.0     5  本科  经验不限  数据服务\n",
       "2924      数据分析员  南京     万得  4-6K     4     6  1.0     5  本科  1年以内  数据服务\n",
       "2925    助理数据分析员  南京     万得  4-8K     4     8  1.0     6  本科  经验不限  数据服务\n",
       "\n",
       "[796 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "薪资小于八千= new_df[(new_df['平均薪资']<8)]\n",
    "薪资小于八千"
   ]
  },
  {
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   "execution_count": 47,
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       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>岗位数</th>\n",
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       "      <td>96</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>郑州</td>\n",
       "      <td>92</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>重庆</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>长沙</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>青岛</td>\n",
       "      <td>107</td>\n",
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       "    城市  岗位数\n",
       "0   上海    1\n",
       "1   南京   61\n",
       "2   厦门   53\n",
       "3   天津   96\n",
       "4   广州   35\n",
       "5   成都   64\n",
       "6   杭州   12\n",
       "7   武汉   65\n",
       "8   深圳    4\n",
       "9   苏州   56\n",
       "10  郑州   92\n",
       "11  重庆   83\n",
       "12  长沙   67\n",
       "13  青岛  107"
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     },
     "execution_count": 47,
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   "source": [
    "薪资小于八千岗位数=pd.DataFrame(薪资小于八千.groupby('城市').平均薪资.count())\n",
    "new_df岗位数=薪资小于八千岗位数.reset_index().rename(columns={\"平均薪资\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "new_df岗位数"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "datas=pd.DataFrame({\"岗位数量\":new_df岗位数[\"岗位数\"]\n",
    "                              ,\"城市\":new_df岗位数[\"城市\"]})\n",
    "fig=px.pie(datas,values=\"岗位数量\",names=\"城市\",title=\"薪资小于八千岗位数量的城市分布情况\",template=\"seaborn\")\n",
    "fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"薪资小于八千岗位数量的城市分布情况.html\",auto_open=False)\n",
    "with open(\"薪资小于八千岗位数量的城市分布情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 薪资大于八千"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
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       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
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       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
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       "      <td>20</td>\n",
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       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
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       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
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       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
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       "      <th>2901</th>\n",
       "      <td>用户数据分析 (MJ002281)</td>\n",
       "      <td>南京</td>\n",
       "      <td>SHEIN</td>\n",
       "      <td>10-15K</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
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       "      <td>12</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>南京</td>\n",
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       "      <td>8-13K</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
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       "      <td>数据分析师</td>\n",
       "      <td>南京</td>\n",
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       "      <td>8-16K</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
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       "      <td>本科</td>\n",
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       "      <th>2908</th>\n",
       "      <td>电商数据分析师 (MJ002279)</td>\n",
       "      <td>南京</td>\n",
       "      <td>SHEIN</td>\n",
       "      <td>10-15K</td>\n",
       "      <td>10</td>\n",
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       "      <td>10</td>\n",
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       "      <td>电子商务</td>\n",
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       "                      职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  \\\n",
       "0                   数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40   \n",
       "1                   数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43   \n",
       "3                   数据分析  北京     一亩田      15-25K    15    25  1.000000    20   \n",
       "4                  数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35   \n",
       "5                   数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26   \n",
       "...                  ...  ..     ...         ...   ...   ...       ...   ...   \n",
       "2901   用户数据分析 (MJ002281)  南京   SHEIN      10-15K    10    15  1.000000    12   \n",
       "2906               数据分析师  南京   阿博茨科技       8-13K     8    13  1.000000    10   \n",
       "2907               数据分析师  南京    满帮集团       8-16K     8    16  1.000000    12   \n",
       "2908  电商数据分析师 (MJ002279)  南京   SHEIN      10-15K    10    15  1.000000    12   \n",
       "2915  游戏数据分析师 (MJ002282)  南京   SHEIN      10-15K    10    15  1.000000    12   \n",
       "\n",
       "      学历   工作经验   行业标签  \n",
       "0     本科  5-10年     银行  \n",
       "1     博士   1-3年   其他行业  \n",
       "3     本科   3-5年    O2O  \n",
       "4     硕士  5-10年     银行  \n",
       "5     本科   1-3年    互联网  \n",
       "...   ..    ...    ...  \n",
       "2901  本科   1-3年   电子商务  \n",
       "2906  本科   3-5年    互联网  \n",
       "2907  本科   1-3年  移动互联网  \n",
       "2908  本科   3-5年   电子商务  \n",
       "2915  本科   3-5年   电子商务  \n",
       "\n",
       "[1908 rows x 11 columns]"
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     "execution_count": 50,
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   "source": [
    "薪资大于八千= new_df[(new_df['平均薪资']>8)]\n",
    "薪资大于八千"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>成都</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>杭州</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>武汉</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>深圳</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>苏州</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>郑州</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>重庆</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>长沙</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>青岛</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    城市  岗位数\n",
       "0   上海  187\n",
       "1   北京  191\n",
       "2   南京  121\n",
       "3   厦门  123\n",
       "4   天津   83\n",
       "5   广州  149\n",
       "6   成都  108\n",
       "7   杭州  181\n",
       "8   武汉  120\n",
       "9   深圳  188\n",
       "10  苏州  131\n",
       "11  郑州   71\n",
       "12  重庆   79\n",
       "13  长沙  120\n",
       "14  青岛   56"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "薪资大于八千岗位数=pd.DataFrame(薪资大于八千.groupby('城市').平均薪资.count())\n",
    "new_df岗位数=薪资大于八千岗位数.reset_index().rename(columns={\"平均薪资\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "new_df岗位数"
   ]
  },
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   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "datas=pd.DataFrame({\"岗位数量\":new_df岗位数[\"岗位数\"]\n",
    "                              ,\"城市\":new_df岗位数[\"城市\"]})\n",
    "fig=px.pie(datas,values=\"岗位数量\",names=\"城市\",title=\"薪资大于八千岗位数量的城市分布情况\",template=\"seaborn\")\n",
    "fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"薪资大于八千岗位数量的城市分布情况.html\",auto_open=False)\n",
    "with open(\"薪资大于八千岗位数量的城市分布情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从以上饼图可以看到，各城市间薪资水平差异非常大。\n",
    "* 岗位人数最多的薪资阶段为4-6k，而且这些岗位大多分布在中西部城市。\n",
    "* 薪资水平小于8k的岗位主要集中分布于青岛、天津、郑州、重庆、成都、武汉等中西部城市；\n",
    "而北上广深杭等城市薪资水平小于8K的特别少。\n",
    "* 而薪资水平大于8K的岗位分布区别则非常明显，北上广深杭的所占比重直接升至7%以上，青岛、郑州等城市所占比重则减少到3%左右。\n",
    "* 由此可见，低薪岗位主要分布于中西部城市；高薪岗位主要分布在东部城市，特别是一线城市。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选取薪资水平较高的五个城市进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1=new_df[new_df.城市=='北京']['平均薪资']\n",
    "data2=new_df[new_df.城市=='上海']['平均薪资']\n",
    "data3=new_df[new_df.城市=='广州']['平均薪资']\n",
    "data4=new_df[new_df.城市=='深圳']['平均薪资']\n",
    "data5=new_df[new_df.城市=='杭州']['平均薪资']\n",
    "plt.figure(figsize=(12,6),dpi=80)\n",
    "plt.boxplot([data1,data2,data3,data4,data5],labels=['北京','上海','广州','深圳','杭州'], \n",
    "            flierprops={'marker':'o','markerfacecolor':'r','color':'k'},\n",
    "            patch_artist=True,boxprops={'color':'k','facecolor':'#FFFACD'})\n",
    "ax=plt.gca()\n",
    "ax.patch.set_facecolor('#FFFAFA')\n",
    "ax.patch.set_alpha(0.8)\n",
    "plt.title('主要城市薪资水平箱线图',fontsize=15)\n",
    "plt.ylabel('薪资(单位：k)',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可以发现，北京的薪资水平是最高的，无论是最低值还是最高值，而且均分布在较高的水平\n",
    "* 相对来说，杭州上海深圳两级分化最为严重"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同学历的薪资和岗位情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同学历的薪资情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "xueli=list(new_df.平均薪资.groupby(new_df['学历']).agg(['mean','median']).reset_index().iloc[:,0])\n",
    "mean=list(new_df.平均薪资.groupby(new_df['学历']).agg(['mean','median']).reset_index().iloc[:,1])\n",
    "median=list(new_df.平均薪资.groupby(new_df['学历']).agg(['mean','median']).reset_index().iloc[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "rgb(153,204,153)"
         },
         "name": "平均值",
         "type": "bar",
         "x": [
          "不限",
          "中技",
          "博士",
          "大专",
          "本科",
          "硕士",
          "高中"
         ],
         "y": [
          12.378378378378379,
          6,
          37.333333333333336,
          8.032854209445585,
          18.190896423594985,
          19.85211267605634,
          5.125
         ]
        },
        {
         "marker": {
          "color": "rgb(204,102,153)"
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       "                            \n",
       "var gd = document.getElementById('93c76dcf-ea32-4b95-9cca-8caabe8671c8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=xueli, \n",
    "    y=mean,             \n",
    "    name='平均值',\n",
    "    marker_color='rgb(153,204,153)'\n",
    "                \n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=xueli, \n",
    "    y=median,             \n",
    "    name='中位数',\n",
    "    marker_color='rgb(204,102,153)',\n",
    "   \n",
    "                \n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='不同学历的薪资对比图',  # 整个图的标题\n",
    "    xaxis_tickfont_size=12,   # x轴字体大小\n",
    "    legend=dict(\n",
    "#         x=0,  # 图例的位置\n",
    "#         y=1,\n",
    "#         bgcolor='rgba(255, 255, 255, 0)',  # 图例的背景色\n",
    "        bordercolor='rgba(2, 255, 255, 0)'   # 边缘颜色\n",
    "    ),\n",
    "    barmode='group',  # 柱状图模式\n",
    "    bargap=0.4,  # 组间距离\n",
    "    bargroupgap=0  # 组内距离\n",
    "    )\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同学历薪资对比图.html\",auto_open=False)\n",
    "with open(\"不同学历薪资对比图.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从学历方面来看，本科是一个分水岭\n",
    "* 是否是本科，对薪资水平的影响还是很大的\n",
    "* 而本科与硕士之间并没有特别大的差距\n",
    "* 博士的薪资水平远远高于其他学历"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同学历的岗位情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>城市</th>\n",
       "      <th>公司</th>\n",
       "      <th>薪资</th>\n",
       "      <th>最低薪资</th>\n",
       "      <th>最高薪资</th>\n",
       "      <th>奖金率</th>\n",
       "      <th>平均薪资</th>\n",
       "      <th>学历</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>行业标签</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>中信百信银行</td>\n",
       "      <td>25-40K·15薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>40</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>BOSS直聘</td>\n",
       "      <td>25-40K·16薪</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>43</td>\n",
       "      <td>博士</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>其他行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>一亩田</td>\n",
       "      <td>15-25K</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>O2O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析岗</td>\n",
       "      <td>北京</td>\n",
       "      <td>建信金科</td>\n",
       "      <td>20-40K·14薪</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>35</td>\n",
       "      <td>硕士</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>银行</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京新氧万维</td>\n",
       "      <td>15-30K·14薪</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>26</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>互联网</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>数据分析师（经济）</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>（金融）数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-6K</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年以内</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>助理数据分析员</td>\n",
       "      <td>南京</td>\n",
       "      <td>万得</td>\n",
       "      <td>4-8K</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>本科</td>\n",
       "      <td>经验不限</td>\n",
       "      <td>数据服务</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2880 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             职位  城市      公司          薪资  最低薪资  最高薪资       奖金率  平均薪资  学历  \\\n",
       "0          数据分析  北京  中信百信银行  25-40K·15薪    25    40  1.250000    40  本科   \n",
       "1          数据分析  北京  BOSS直聘  25-40K·16薪    25    40  1.333333    43  博士   \n",
       "3          数据分析  北京     一亩田      15-25K    15    25  1.000000    20  本科   \n",
       "4         数据分析岗  北京    建信金科  20-40K·14薪    20    40  1.166667    35  硕士   \n",
       "5          数据分析  北京  北京新氧万维  15-30K·14薪    15    30  1.166667    26  本科   \n",
       "...         ...  ..     ...         ...   ...   ...       ...   ...  ..   \n",
       "2921    助理数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2922  数据分析师（经济）  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2923  （金融）数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2924      数据分析员  南京      万得        4-6K     4     6  1.000000     5  本科   \n",
       "2925    助理数据分析员  南京      万得        4-8K     4     8  1.000000     6  本科   \n",
       "\n",
       "       工作经验  行业标签  \n",
       "0     5-10年    银行  \n",
       "1      1-3年  其他行业  \n",
       "3      3-5年   O2O  \n",
       "4     5-10年    银行  \n",
       "5      1-3年   互联网  \n",
       "...     ...   ...  \n",
       "2921   经验不限  数据服务  \n",
       "2922   经验不限  数据服务  \n",
       "2923   经验不限  数据服务  \n",
       "2924   1年以内  数据服务  \n",
       "2925   经验不限  数据服务  \n",
       "\n",
       "[2880 rows x 11 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>学历</th>\n",
       "      <th>岗位数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>不限</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中技</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>博士</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大专</td>\n",
       "      <td>487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>本科</td>\n",
       "      <td>2153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>硕士</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>高中</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   学历   岗位数\n",
       "0  不限    74\n",
       "1  中技    10\n",
       "2  博士     6\n",
       "3  大专   487\n",
       "4  本科  2153\n",
       "5  硕士   142\n",
       "6  高中     8"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=pd.DataFrame(new_df.groupby('学历').职位.count())\n",
    "不同学历岗位数量=data2.reset_index().rename(columns={\"职位\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "不同学历岗位数量"
   ]
  },
  {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "datas=pd.DataFrame({\"岗位数量\":不同学历岗位数量[\"岗位数\"]\n",
    "                              ,\"学历\":不同学历岗位数量[\"学历\"]})\n",
    "fig=px.pie(datas,values=\"岗位数量\",names=\"学历\",title=\"不同学历岗位数量情况\",template=\"seaborn\")\n",
    "fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同学历岗位数量情况.html\",auto_open=False)\n",
    "with open(\"不同学历岗位数量情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从市场需求来看，要求本科占据绝大多数\n",
    "* 该岗位对学历的门槛并不是很高，要求硕士的较少，而博士则是凤毛麟角\n",
    "* 不过这也从侧面反映了社会上学历的分布状况，本科生及大专生占绝对多数\n",
    "* 虽然市场需求量大，但是本科生就业压力也很大"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同工作经验的薪资和岗位情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同工作经验的薪资情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "工作经验=list(new_df.平均薪资.groupby(new_df['工作经验']).agg(['mean','median']).reset_index().iloc[:,0])\n",
    "mean=list(new_df.平均薪资.groupby(new_df['工作经验']).agg(['mean','median']).reset_index().iloc[:,1])\n",
    "median=list(new_df.平均薪资.groupby(new_df['工作经验']).agg(['mean','median']).reset_index().iloc[:,2])"
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   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=工作经验, \n",
    "    y=mean,             \n",
    "    name='平均值',\n",
    "    marker_color='rgb(153, 102, 153)'\n",
    "                \n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=工作经验, \n",
    "    y=median,             \n",
    "    name='中位数',\n",
    "    marker_color='rgb(255, 255, 125)',\n",
    "   \n",
    "                \n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='各工作年限薪资均值及中位数比较图',  # 整个图的标题\n",
    "    xaxis_tickfont_size=12,   # x轴字体大小\n",
    "    legend=dict(\n",
    "#         x=0,  # 图例的位置\n",
    "#         y=1,\n",
    "#         bgcolor='rgba(255, 255, 255, 0)',  # 图例的背景色\n",
    "        bordercolor='rgba(2, 255, 255, 0)'   # 边缘颜色\n",
    "    ),\n",
    "    barmode='group',  # 柱状图模式\n",
    "    bargap=0.4,  # 组间距离\n",
    "    bargroupgap=0  # 组内距离\n",
    "    )\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同工作经验的薪资情况.html\",auto_open=False)\n",
    "with open(\"不同工作经验的薪资情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 应届生的工资还是比较低的，和工作经验一年内的差距并不大\n",
    "* 但是随着工作年限的增加，迈过3年工作经验这个门槛，工资水平将直线上升\n",
    "* 说明这个岗位是一个需要累积丰富经验的岗位"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同工作经验的岗位情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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   "source": [
    "new_df_工作经验=new_df[[\"工作经验\",\"职位\"]]\n",
    "new_df_gzjy=pd.DataFrame(new_df_工作经验.groupby('工作经验').职位.count())\n",
    "new_df_岗位数=new_df_gzjy.reset_index().rename(columns={\"职位\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "new_df_gzjy_s=pd.DataFrame({\"工作经验\":new_df_岗位数[\"工作经验\"]\n",
    "                              ,\"岗位数量\":new_df_岗位数[\"岗位数\"]})\n",
    "工作经验分析=new_df_gzjy_s.sort_values(by=[\"岗位数量\"],ascending=False)\n",
    "fig=px.bar(工作经验分析,x=\"工作经验\",y=\"岗位数量\",color=\"工作经验\",title=\"不同工作经验的岗位数量情况\",template=\"seaborn\")\n",
    "#fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同工作经验的岗位情况.html\",auto_open=False)\n",
    "with open(\"不同工作经验的岗位情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
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  {
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   "metadata": {},
   "source": [
    "### 数据分析结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 我们发现，市场上对于工作经验的要求还是很强烈的\n",
    "* 1-5年的经验要求占绝大多数\n",
    "* 而应届生和10年以上的需求则非常少\n",
    "* 工作经验是入职成功与否的关键因素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同行业的薪资和岗位情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同行业的薪资情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "hangye=list(new_df.平均薪资.groupby(new_df['行业标签']).agg(['mean','median']).reset_index().iloc[:,0])\n",
    "mean=list(new_df.平均薪资.groupby(new_df['行业标签']).agg(['mean','median']).reset_index().iloc[:,1])\n",
    "median=list(new_df.平均薪资.groupby(new_df['行业标签']).agg(['mean','median']).reset_index().iloc[:,2])"
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     },
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    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=hangye, \n",
    "    y=mean,             \n",
    "    name='平均值',\n",
    "    marker_color='#408080'\n",
    "                \n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=hangye, \n",
    "    y=median,             \n",
    "    name='中位数',\n",
    "    marker_color='#984B4B',\n",
    "   \n",
    "                \n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title='不同行业的薪资对比图',  # 整个图的标题\n",
    "    xaxis_tickfont_size=14,   # x轴字体大小\n",
    "    legend=dict(\n",
    "#         x=0,  # 图例的位置\n",
    "#         y=1,\n",
    "#         bgcolor='rgba(255, 255, 255, 0)',  # 图例的背景色\n",
    "        bordercolor='rgba(2, 255, 255, 0)'   # 边缘颜色\n",
    "    ),\n",
    "    barmode='group',  # 柱状图模式\n",
    "    bargap=0.4,  # 组间距离\n",
    "    bargroupgap=0  # 组内距离\n",
    "    )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同行业的薪资情况.html\",auto_open=False)\n",
    "with open(\"不同行业的薪资情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同行业的岗位情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "new_df_行业=new_df[[\"行业标签\",\"职位\"]]\n",
    "new_df_hybq=pd.DataFrame(new_df_行业.groupby('行业标签').职位.count())\n",
    "new_df_岗位数=new_df_hybq.reset_index().rename(columns={\"职位\":\"岗位数\"})\n",
    "#岗位数=岗位数.iloc[:,1]\n",
    "new_df_hybq_s=pd.DataFrame({\"行业\":new_df_岗位数[\"行业标签\"]\n",
    "                              ,\"岗位数量\":new_df_岗位数[\"岗位数\"]})\n",
    "工作经验分析=new_df_hybq_s.sort_values(by=[\"岗位数量\"],ascending=False)\n",
    "fig=px.bar(工作经验分析,x=\"行业\",y=\"岗位数量\",color=\"行业\",title=\"不同行业的岗位数量情况\",template=\"seaborn\")\n",
    "#fig.update_traces(textposition=\"inside\",textinfo=\"value+percent+label\")#标签位置放在里面，标签信息包含值，百分比，标签\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.offline.plot(fig, filename=\"不同行业的岗位情况.html\",auto_open=False)\n",
    "with open(\"不同行业的岗位情况.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "    plot_all1 = \"\".join(f.readlines())"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据结果分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通过上图我们可以发现，互联网行业和电子商务对该岗位的需求非常大，同时薪资水平相比于其他行业也高了很多。\n",
    "* 而其他传统行业相对来说，无论是需求量还是薪资水平，都低一些"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 通过以上分析，可以发现，如果要成功进入数据分析行业，应该往北上广深行这几个城市考虑，这些城市的薪资水平高。\n",
    "* 而行业方面应该着眼于互联网及电子商务等新兴行业，这些行业对该岗位的需求都比较大，而且薪资水平也普遍高\n",
    "* 工作经验对于该岗位而言非常重要，能否成功进入数据分析行业的关键门槛在于是否有工作经验，与薪资水平呈正相关\n",
    "* 而成功进入数据分析行业以后，只有在迈过3年这个坎，才会有比较大的突破"
   ]
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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